Encounters with Doubt: A Dashboard for UFO Skeptics

Timeline:3 weeks

Final Deliverable:Interactive Dashboard using Tableau

Overview

This visualization is an exploration of a dataset of UFO reports from across the United States (English Speaking World). For this vis we worked to understand the interests of UFO skeptics to create a project that explores possible correlations between things frequently mistaken for UFO sighting. While changing deeply held beliefs was not in our scope, the project gave users a chance to learn more about a subject and explore UFO sightings data.

This projects highlights an opportunity to work with data scientists and look at large datasets to create meaningful information

Challenge

We began with a dataset of over 80,000 UFO sightings from around the world that we found on Kaggle. There was already a dashboard at ufostalker.com, which used this UFO sightings data so we wanted to map the data in different ways. Their visualization is geared towards UFO enthusiasts. We decided to tailor our project to alien skeptics in hopes of uncovering a new perspective from the data. After getting feedback from user research on how our data could bring value to our users, we found that all of our users believed that people tend to mistake alien space crafts for rockets and airplanes. We used this insight to try to seek correlations between airplanes and sightings by adding airport data into a heat map (see fig 1). Next, we wanted to explore popular UFO movies and the number of UFO reports (see fig 2). Finally, we added some additional visualizations to round out the picture of what people report when they claim to see a UFO.

fig. 1

fig. 2

Building the Vis

We interviewed 3 alien skeptics to get a sense of what might confirm or disprove their belief in aliens. Users mentioned that they believed UFO sightings were usually other things in the air such as airplanes, weather events. We decided to focus on potential causes of alien sightings. We started broad creating a series of sketches geared at our users, skeptics. Our focus was:

01. Create easy comparisions

02. Show the data

03. Encode information clearly

User Testing

We created a draft of a visualization to gather user feedback. We tested on 3 users (skeptics in their 20s). This helped us locate a number of problems. For our UFO sightings map, users were concerned that the correlations between frequency of airports and UFO sightings “hot-spots” were mostly apparent due to those two co-occurring in highly populated areas. In our final heat map, we added an overlay of sightings per capita based on this feedback. We also limited our map to the U.S. only to make it more easily readable. For our shape stacked bar chart, users had trouble understanding the story behind the bar chart and thought the word “shape” wasn’t a clear way to represent the appearance of the UFO, so we changed it to “type”. We also plotted influential alien movies that mapped on well to certain spikes in shape type to help us tell a better story about the correlation between an increase in the appearance of a certain shape and the occurrence of that same shape in a popular alien movie released that year. Our duration chart was confusing to most of our users. A user accurately pointed out that it was not very effective because there were not a lot of changes in the length of UFO sightings over time, so we decided to change the visualization to a simple pie chart with only four slices to make it crystal clear that most sightings occurred for less than five minutes (the length of an airplane sighting).

Options 2 was deemed hard to read and confusing.

Users preferred Option 2 with encounter lengths by percentages because the number of UFO sightings reported changed greatly across time. Percentages were a more affective way to show duration.

Final Product

In our final dashboard (see at the top of the page) we tried to show a variety of different interesting data. We did discover some compelling evidence for skeptics. We launched a webpage with the final visualizations.

Some Potential Findings From the Visualization

01. The number of "disk" shape UFO sightings has gone down since that shape has decreased in movies

“sightings have become more ambiguous [over time]”

-User #2

02. There was a correlation between sightings and proximity to airports, but the population was also higher near airports so it was hard to draw conclusions

03. Sightings are generally at times on the weekends when people tend to be drunk, but that is also when the most people are out at night.

“I see sightings correlate closely with airports and I question methodology. It is probably based on human density, and can’t draw conclusions from this.”

- User #3

An image of the heat map. For the actual map see the website link.

Takeaways

I really enjoyed learning from data scientists and collaborating with people from other disciplines. The class was geared towards teaching students what pre-attentively makes sense to humans so they have to do the least amount of work to understand information. There were a number of readings and papers that explored the writing of giants like Tufte and Heer and Shneiderman. I was surprised to find that I often return to these hierarchical data classifications when designing. It was also great to have more experience user testing.

If we had been able to take the project further it would have been helpful to explore other map visualizations. The map provided by Tableau lacks wayfinding information such as major cities, so it is not very user friendly. The heat map we used from a Python script looked interesting, but it was not the most affective way to encode the data. Users had some trouble understanding it. Our visualization would be improved by showing flight patterns and even focus on plane visibility rather than airports. This would increase the chance of seeing patterns of UFO sightings in certain locations. With this self-reporting data there will always be some questions, but it was a good way to practice working with dashboards and Tableau.